Image super-resolution (SR) is a preferred approach to achieving high-resolution MR images because they are typically achieved in real world at the expense of reduced signal-to-noise ratio and/or increased imaging time. A novel deep residual network (DRN) with multi-unit upsampling learning is designed for MR image SR. The multi-unit upsampling learning mechanism involves multi-unit upsampling and adaptive learning. The designed DRN performs the SR task in the LR space to accelerate the network via an upsampling strategy at the late stage of the network architecture. A multi-unit upsampling strategy is proposed to transmit lost information in each residual unit and to accumulate the upscaled feature maps achieved by different residual units. An adaptive learning strategy following multi-unit upsampling is utilized to potentially discover the contributions of these upscaled feature maps to high-resolution MR image reconstruction by adaptively assigning different significance weights to the intermediate predictions. The proposed DRN achieves a fair good reconstruction performance, which is superior to some state-of-the-art deep-learning-based methods.

图像超分辨率（SR）是获得高分辨率MR图像的一种首选方法，因为它们通常是在现实世界中以降低信噪比和/或增加成像时间为代价而实现的。针对MR图像SR设计了一种具有多单元上采样学习的新型深度残差网络（DRN）。多单元上采样学习机制涉及多单元上采样和自适应学习。设计的DRN在LR空间中执行SR任务，以在网络体系结构的后期通过上采样策略来加速网络。提出了一种多单元升采样策略，以在每个残差单元中传输丢失的信息，并累积由不同残差单元实现的放大特征图。通过自适应地将不同的重要权重分配给中间预测，利用多单元上采样之后的自适应学习策略来潜在地发现这些放大后的特征图对高分辨率MR图像重建的贡献。提出的DRN实现了相当好的重建性能，优于某些基于最新的深度学习方法。